[1]} propose a coarse-to-fine QMDS modeling framework to address the challenge. As shown in Figure REF (b), the approach employs progressively more accurate modules for estimating whether a sentence is relevant to a query (relevance estimator), likely to contain answers to the query (evidence estimator), and should be included in the summary (centrality estimator). The authors argue that compared to classic methods where all sentences are encoded and scored simultaneously (Figure REF (a)), the coarse-to-fine approach is more efficient, especially for highly interactive CIR scenarios, since at each step the model processes a decreasing number of sentences and the computational cost does not increase with the size of the original input \(\mathcal {D}\) .
